The fifth-generation (5G) wireless network provides high-rate, ultra-low latency, and high-reliability connections that can meet the industrial IoT requirements in factory automation, especially for swarm robotics communication. In this paper, we address 5G service provisioning in an automated warehouse scenario where swarm robotics is controlled by an industrial controller that provides routing and job instructions over the 5G network. Leveraging the coordinated multipoint (CoMP), we formulate a joint CoMP clustering and 5G ultra-reliable low-latency communication (URLLC) beamforming design problem to control the robots that move around the automated warehouse for goods storage with the planned reference tracks. Traditional iterative optimization approaches are impractical in such dynamic wireless environments due to high computational time. We propose a game-theoretic CoMP clustering algorithm combined with the Proximal Policy Optimization method to obtain a stationary solution closed to that of the exhaustive search algorithm considered as the global optimal solution.
Game Theoretic Reinforcement Learning Framework For Industrial Internet of Things
tai.ho2022-05-02T18:54:06+00:00May 2nd, 2022|2022, Publications|0 Comments
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